Beta barrel trans-membrane proteins: enhanced prediction using a Bayesian approach

Paul D. Taylor, Christopher P. Toseland, Teresa K. Attwood, Darren R. Flower

Research output: Contribution to journalArticle

Abstract

Membrane proteins, which constitute approximately 20% of most genomes, form two main classes: alpha helical and beta barrel transmembrane proteins. Using methods based on Bayesian Networks, a powerful approach for statistical inference, we have sought to address beta-barrel topology prediction. The beta-barrel topology predictor reports individual strand accuracies of 88.6%. The method outlined here represents a potentially important advance in the computational determination of membrane protein topology.
Original languageEnglish
Pages (from-to)231-233
Number of pages3
JournalBioinformation
Volume1
Issue number6
Early online date7 Oct 2006
Publication statusPublished - 2006

Fingerprint

Bayes Theorem
Membrane Proteins
Genome
Proteins

Bibliographical note

This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.

Keywords

  • beta barrel transmembrane protein
  • prokaryotic membrane proteins
  • Bayesian networks
  • prediction method
  • sub-cellular location

Cite this

Taylor, P. D., Toseland, C. P., Attwood, T. K., & Flower, D. R. (2006). Beta barrel trans-membrane proteins: enhanced prediction using a Bayesian approach. Bioinformation, 1(6), 231-233.
Taylor, Paul D. ; Toseland, Christopher P. ; Attwood, Teresa K. ; Flower, Darren R. / Beta barrel trans-membrane proteins : enhanced prediction using a Bayesian approach. In: Bioinformation. 2006 ; Vol. 1, No. 6. pp. 231-233.
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Taylor, PD, Toseland, CP, Attwood, TK & Flower, DR 2006, 'Beta barrel trans-membrane proteins: enhanced prediction using a Bayesian approach', Bioinformation, vol. 1, no. 6, pp. 231-233.

Beta barrel trans-membrane proteins : enhanced prediction using a Bayesian approach. / Taylor, Paul D.; Toseland, Christopher P.; Attwood, Teresa K.; Flower, Darren R.

In: Bioinformation, Vol. 1, No. 6, 2006, p. 231-233.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Beta barrel trans-membrane proteins

T2 - enhanced prediction using a Bayesian approach

AU - Taylor, Paul D.

AU - Toseland, Christopher P.

AU - Attwood, Teresa K.

AU - Flower, Darren R.

N1 - This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.

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KW - prokaryotic membrane proteins

KW - Bayesian networks

KW - prediction method

KW - sub-cellular location

UR - http://www.bioinformation.net/001/001.htm

M3 - Article

C2 - 17597895

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Taylor PD, Toseland CP, Attwood TK, Flower DR. Beta barrel trans-membrane proteins: enhanced prediction using a Bayesian approach. Bioinformation. 2006;1(6):231-233.